Recent Advances in Selection Hyper-heuristics

Hyper-Heuristics

Solving urban transit route design problem using selection hyper-heuristics

What makes a VRP solution good? The generation of problem-specific knowledge for heuristics

Search techniques have been successfully applied for solving different software testing problems. However, choosing, implementing and configuring a search technique can be hard tasks. To reduce efforts spent in such tasks, this paper presents an offline hyper-heuristic named GEMOITO, based on Grammatical Evolution (GE). The goal is to automatically generate a Multi-Objective Evolutionary Algorithm (MOEA) to solve the Integration and Test Order (ITO) problem. The MOEAs are distinguished by components and parameters values, described by a grammar. The proposed hyper-heuristic is compared to conventional MOEAs and to a selection hyper-heuristic used in related work. Results show that GEMOITO can generate MOEAs that are statistically better or equivalent to the compared algorithms.

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Grammatical Evolution for the Multi-Objective Integration and Test Order Problem

A common problem when applying heuristics is that they often perform well on some problem instances, but poorly on others. We develop a hyper-heuristic approach, using Grammatical Evolution (GE), to generate heuristics for the Vehicle Routing Problem (VRP). Through a series of experiments we develop an approach that leads to solutions of acceptable quality to Vehicle Routing Problem instances with only limited prior knowledge of the problem to be solved.

Hyper-heuristics, grammatical evolution and the capacitated vehicle routing problem

A common problem when applying heuristics is that they often perform well on some problem instances, but poorly on others. We work towards developing a hyper-heuristic that manages delivery of good quality solutions to Vehicle Routing Problem instances with only limited prior knowledge of the problem domain to be solved. This paper develops a hyper-heuristic, using Grammatical Evolution, to generate and apply heuristics that develop good solutions. Through a series of experiments we expand and refine the technique, achieving good quality results on 40 well known Capacitated Vehicle Routing Problem instances.

Developing a Hyper-Heuristic Using Grammatical Evolution and the Capacitated Vehicle Routing Problem

Earlier research has shown that an adaptive hyper-heuristic can be a successful approach to solving combinatorial optimisation problems. By using a pairing of an operator (low-level heuristic) selection vector and a solution acceptance criterion, an adaptive hyper-heuristic can manage development of a “good” solution within an unseen low-level problem domain in a commercially realistic computational time. However not all selection vectors and solution acceptance criteria pairings deliver competitive results when faced with differing problem instance features and computational time limits. We evaluate pairings of six different operator selection vectors and eight solution acceptance criteria, and monitor the performance of the adaptive hyper-heuristic when applying each pairing to a set of Capacitated Vehicle Routing Problem instances of the same size but with different features. The results show that a few pairings of operator selection vector and acceptance criterion perform consistently well, while others require a longer computational time to deliver competitive results. We also investigate some of the features of a problem instance that may influence the performance of the selection vector and acceptance criterion pairings.

Hyper-heuristic Operator Selection and Acceptance Criteria

Developing and managing a general method of solving combinatorial optimisation problems reduces the need for expensive human experts when solving previously unseen variations to common optimisation problems. A hyper-heuristic provides such a method. Each hyper-heuristic has its own strengths and weaknesses and we research how these properties can be managed. We construct and compare simplified versions of two existing hyper-heuristics adaptive and grammar-based, and analyse how each handles the trade-off between computation speed and quality of the solution. We test the two hyper-heuristics on seven different problem domains using the HyFlex framework. We conclude that both hyper-heuristics successfully identify and manipulate low-level heuristics to generate "good" solutions of comparable quality, but the adaptive hyper-heuristic consistently achieves this in a shorter computational time than the grammar based hyper-heuristic.

Richard J. Marshall

Papers

Hyper-heuristics, grammatical evolution and the capacitated vehicle routing problem

Developing a Hyper-Heuristic Using Grammatical Evolution and the Capacitated Vehicle Routing Problem

Hyper-heuristic Operator Selection and Acceptance Criteria

A Comparison between Two Evolutionary Hyper-Heuristics for Combinatorial Optimisation